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Article

Identification of Mint Scents Using a QCM Based E-Nose

1
Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Hermann-Von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, Germany
2
Light Technology Institute, Karlsruhe Institute of Technology, Engesserstraße 13, 76131 Karlsruhe, Germany
3
Karlsruhe Institute of Technology, Botanical Institute, Molecular Cell Biology, Fritz-Haber-Weg, 76131 Karlsruhe, Germany
4
Institute of Microstructure Technology, 223, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, Eggenstein-Leopoldshafen, 76344 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Kien Wen Sun
Chemosensors 2021, 9(2), 31; https://doi.org/10.3390/chemosensors9020031
Received: 30 December 2020 / Revised: 29 January 2021 / Accepted: 31 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Organic-Inorganic Hybrid Chemo- and Bio-Sensors)
Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner. View Full-Text
Keywords: mint; plant volatiles; electronic nose; principal component analysis; linear discriminant analysis; k-nearest-neighbors analysis mint; plant volatiles; electronic nose; principal component analysis; linear discriminant analysis; k-nearest-neighbors analysis
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MDPI and ACS Style

Okur, S.; Sarheed, M.; Huber, R.; Zhang, Z.; Heinke, L.; Kanbar, A.; Wöll, C.; Nick, P.; Lemmer, U. Identification of Mint Scents Using a QCM Based E-Nose. Chemosensors 2021, 9, 31. https://doi.org/10.3390/chemosensors9020031

AMA Style

Okur S, Sarheed M, Huber R, Zhang Z, Heinke L, Kanbar A, Wöll C, Nick P, Lemmer U. Identification of Mint Scents Using a QCM Based E-Nose. Chemosensors. 2021; 9(2):31. https://doi.org/10.3390/chemosensors9020031

Chicago/Turabian Style

Okur, Salih, Mohammed Sarheed, Robert Huber, Zejun Zhang, Lars Heinke, Adnan Kanbar, Christof Wöll, Peter Nick, and Uli Lemmer. 2021. "Identification of Mint Scents Using a QCM Based E-Nose" Chemosensors 9, no. 2: 31. https://doi.org/10.3390/chemosensors9020031

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